This invention relates to improvements in a demand estimation apparatus for estimating a demand such as the traffic volume of elevators in a building and the electric power load of a power station.
The traffic volume of elevators in a building, the electric power load of a power station, or the like (hereinbelow, termed "demand") fluctuate irregularly when closely observed within a period of one day, but remain substantially constant for the same time zones when observed over several days. For example, in an office building, the first floor is often crowded with elevator passengers on their way to their office floors during a short period of time in the morning. In the first half of the lunch hour, many passengers may go from their office floors to a restaurant floor or the first floor, while in the latter half thereof, many passengers may go from the restaurant floor and the first floor to the office floors. On the other hand, at the end of the day the passengers go from their office floors to the first floor when they leave the building. The volume of traffic in the up direction in the morning is nearly equal to the volume of traffic in the down direction in the evening and the volume of traffic becomes very small throughout the night.
In order to deal with the changing traffic in a building with a limited number of elevators, the elevators are usually operated under group supervision. When a hall call is registered anew, an elevator is tentatively assigned to serve this hall call so that the waiting times of all hall calls and the possibility of the full capacity of passengers are predicted to determine service evaluation values for corresponding elevators. In order to execute the demand estimation process, certain traffic data pertaining to the operation of the elevators in the building is required. For example, data on the number of passengers who get on and off each elevator cage at the intermediate floors is required for predicting the possibility of full capacity. However, it is impractical to constantly store such traffic data every short moment since an enormous memory capacity is necessitated. Usually, the required memory size is reduced by dividing the operating period into several time zones, and only the average traffic volume in each time zone is stored. On the other hand, traffic data in a building may also change as a result of changes in personnel organization in the building, and hence, it is difficult to obtain good traffic data from which the demand can be predicted accurately. For this reason, a system has been developed, for example, as disclosed in copending application Ser. No. 473,359 and U.S. Pat. No. 4,524,418, wherein traffic conditions in the building are continuously detected so as to sequentially improve the traffic data.
More specifically, the operating period of one day is divided into K time zones (hereinbelow, termed "sections"), and a time (hereinbelow, termed "boundary time") by which a section k-1 and a section k are bounded is denoted by t.sub.k (k=2, 3, . . . K). Times t.sub.1 and t.sub.K+1 are the starting time and end time of the elevator operation, respectively. The average traffic volume P.sub.k (l) of the section k on the l-th day is supposed to be given by the following equation (1): ##EQU1##
Here, X.sub.k.sup.u (l) is a column vector of F-1 dimensions (where F denotes the number of floors) the elements of which are the number of passengers to get on cages in the up direction at respective floors in the time zone k of the l-th day. Similarly, X.sub.k.sup.d (l), Y.sub.k.sup.u (l) and Y.sub.k.sup.d (l) are column vectors which indicate the number of passengers to get on the cages in the down direction, the number of passengers to get off the cages in the up direction and the number of passengers to get off the cages in the down direction, respectively. The average traffic volume (hereinbelow, termed "average demand") P.sub.k (l) is measured by a passenger-number detector which utilizes load changes during the stoppage of the cages of the elevators and/or industrial television, ultrasonic wave, or the like.
First, it will be considered to sequentially correct the respresentative value of the average demand P.sub.k (l) of each time zone in a case where the boundary time t.sub.K is fixed.
It is known that the columns P.sub.k (1), P.sub.k (2), . . . of the daily average demands will disperse in the vicinity of a certain representative value P.sub.k. Since the magnitude of the representative value P.sub.k is unknown, it needs to be estimated. In this case, there is the possibility that the magnitude itself of the representative value P.sub.k will change. The representative value is therefore predicted by taking a linear weighted average given in Equations (2) and (3) below and applying more weight to the average demand P.sub.k (l) measured latest, than to the other average demands P.sub.k (1), P.sub.k (2), . . . , P.sub.k (l-1). ##EQU2## EQU .lambda..sub.i =a(1-a).sup.l-i ( 3)
Here, P.sub.k (l) is a predicted representative value determined from the average demands P.sub.k (1), . . . , P.sub.k (l) measured up to the l-th day, and P.sub.k (0) is an initial suitable value set in advance. .lambda..sub.i denotes the weight of the average demand P.sub.k (i) measured on the i-th day, and this weight changes depending upon a parameter a. More specifically, an increase in the value of the parameter a results in an estimation in which more weight is applied to the latest measured average demand P.sub.k (l) than to the other average demands P.sub.k (1), . . . P.sub.k (l-1), and in which the predicted representative value P.sub.k (l) quickly follows up the change of the representative value P.sub.k. However, when the value of the parameter a is too large, the predicted representative value drastically changes in a manner to be infuenced by the random variation of daily data. Meanwhile, Equations (2) and (3) can be rewritten as follows: EQU P.sub.k (l)=(1-a)P.sub.k (l-1)+aP.sub.k (l) (4) EQU P.sub.k (0)=P.sub.k (0) (5)
In accordance with the above Equation (4), there is the advantage that the weighted average of Equation (2) can be calculated without storing the observation values P.sub.k (i) (i=1, 2, . . . , l-1) of the average demands in the past.
It is noted, however, that even though the foregoing representative value P.sub.k (k=2, 3, . . . , K) of the average demand of each time zone has been precisely estimated, the deviation thereof from the actual demand becomes large near the demarcating boundary time t.sub.k (k=2, 3, . . . , K) when the boundary time t.sub.k itself is incorrectly set. This large deviation brings about the disadvantage that the predicted calculations of the waiting times, the possibility of the full capacity, etc. become erroneous, so the elevators are not group-supervised as intended.